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Distributed Resource Allocation Based on Queue Balancing in Multihop Cognitive Radio Networks

Platform : java

IEEE Projects Years : 2012 - 13

Distributed Resource Allocation Based on Queue

Balancing in Multihop Cognitive Radio Networks

Abstract:

 

     Cognitive radio is an emerging technology for future wireless communication and networking. Cognitive radio makes it possible for unlicensed/cognitive users or devices to opportunistically utilize the licensed spectrum when it is not occupied by licensed/primary users. It can overcome the drawback of the current static spectrum allocation policy and address the soon-to-be-acute spectrum shortage problem. For multihop wireless networks, cross-layer resource allocation especially distributed resource allocation is a challenging problem. Joint channel allocation, power control, route selection, and congestion control, which affect one another, make the problem even more difficult In this paper, we can use flow control instead of using routing. Flow control is considered for the transmission of data from a source to the corresponding destination.

 

Existing System

           

       Cognitive radio allows unlicensed users to access the licensed spectrum opportunistically (i.e., when the spectrum is left unused by the licensed users) to enhance the spectrum utilization efficiency. For multihop wireless networks, cross-layer resource allocation especially distributed resource allocation is a challenging problem. Joint channel allocation, power control, route selection, and congestion control, which affect one another, make the problem even more difficult.

 

Disadvantage

        

  • It maintains current static spectrum allocation policy.
  • It does not assume fixed link capacity

 

Proposed System

      

            In this paper, flow control is considered for the transmission of data from a source to the corresponding destination. The common methods for flow control are the price- and resource directive decomposition. These methods divide the multicommodity flow problem into single-commodity flow problems and find the paths for each commodity. However, they require a centralized control. Because link capacities are not fixed in our problem setting, but determined by the resource allocation in wireless networks. It proposed another method, called queue-balancing flow control, for the multicommodity flow problem. In this paper, we propose a distributed resource-allocation scheme that meets end-to-end throughput demands for multiple sessions in multihop cognitive radio network.

 

 

Advantage

 

 

  • Each node has the power mask on every channel to protect primary users.
  • It maintains dynamic spectrum allocation policy.
  • It maintains throughput requirement efficiently

 

 

HARDWARE & SOFTWARE REQUIREMENTS

HARDWARE REQUIREMENTS

System                                    :           Pentium IV 2.4 GHz

Hard Disk                   :           40 GB

Monitor                       :           15 VGA Color

Mouse                         :           Logitech

Ram                             :           512 MB

SOFTWARE REQUIREMENTS

Operating System       :           Windows XP Professional

Language                    :            java

        

 Module 1: System model   

 

          Each node is equipped with two radio interfaces, one for transmitting data and the other for receiving data. OFDM is assumed to have been deployed in the network, so multiple channels can be used in each interface. In order to reduce the complexity of resource allocation, several sub carriers are combined to be a channel. If there is no primary user nearby, the Cognitive Radio nodes can transmit with as much power as they can.

 

Module 2:Queue balancing for network flow control­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

       

        The problem we formulated can be modeled as a multicommodity Flow problem. We adopt the queue-balancing flow control, which does not choose paths for each commodity, but pushes the data from sources to the corresponding destinations by using queue potential. There is a queue for each session at both the transmitter and the receiver of each link

         The goal of resource allocation is to balance the queues between neighboring nodes. Because the traffic of the sessions enters the network from their sources and exits the network via their destination nodes, the queue size of the source is always larger than that of the destination for each session. Therefore, balancing the queues can push the data from the sources to the corresponding destinations. Because data queueing lets some data remain in the network, to meet the throughput requirement, the data need to be pumped

into the network at a rate higher than the required rate. Therefore, the data enters the network at the rate

 

Module3: Node Level Resource Allocation

    

         By using the queue-balancing flow control, the problem can be transformed to a resource-allocation problem for each link. Specifically, we need to allocate the channel and power resource for each link so as to maximize the decrease of potential functions in each time. To maximize the decrease of the potential, we first investigate the optimal rate control for a link with fixed capacity. This way, the maximum potential decrease for the link with a given capacity can be used for calculation of the potential decrease under power allocation strategies with the optimal rate control. The optimal power allocation strategies will obtain the optimal capacity allocation for the link, which also achieves the maximum potential decrease. Similarly, the maximum potential decrease based on the optimal rate control and power allocation can be used in the channel allocation.

        

       The water-filling allocation between the channels within a link can achieve the optimal capacity, even if there are power masks for the channels in cognitive radio networks. The distributed resource allocation algorithm describes the information exchange process at link. The proposed distributed coordination of Channel allocation yields interference-free channel allocations. By repeating the proposed distributed coordination of channel allocations until all nodes either transmit INFO because of or receive INFO, the resultant channel allocation is not optimal, but is a maximum set in the sense that the transmitting link set is not contained by any other transmitting set for each channel.

 

 

Module 4: Network Level Resource Allocation

 

          Based on the above analysis, we propose a node-based distributed algorithm for joint flow control, channel allocation, and power control in CR networks .In step1 update the queue sizes at each node. Balance the queue sizes within each node for all of its sessions. In step send the control information needed for resource allocation. In step3 determine the resource allocation and exchange INFO and REQ, if necessary. In Step 4 Adjust the channel allocation strategy and determine the corresponding power and data rate for each session. Send OCCUPY for new channel occupation.

         If the network capacity is not large enough to transmit all traffic, some adjustment at the sources of each session is necessary. The adjustment method depends on the properties of the sessions’ services. When the overflow queue is too large at the traffic sources, the source nodes should decrease the data rate into the network, if possible, or reject some sessions if the throughput requirements are strict.

      

        

  

 

 

 

 

 

 

 

 

 

 

 

 



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